// 高级AI推荐引擎 - 基于深度学习和机器学习的智能推荐系统
// 集成协同过滤、内容过滤、深度学习、实时推荐等先进技术

class AdvancedAIEngine {
    constructor() {
        this.model = new DeepLearningModel();
        this.collaborativeFilter = new CollaborativeFiltering();
        this.contentFilter = new ContentBasedFiltering();
        this.realTimeEngine = new RealTimeRecommendation();
        this.sentimentAnalyzer = new SentimentAnalyzer();
        this.abTestManager = new ABTestManager();
        this.recommendationCache = new Map();
        this.userEmbeddings = new Map();
        this.itemEmbeddings = new Map();
        this.initializeAdvancedFeatures();
    }

    // 初始化高级功能
    initializeAdvancedFeatures() {
        this.loadPretrainedModels();
        this.setupRealTimeProcessing();
        this.initializeABTests();
        this.startContinuousLearning();
    }

    // 深度学习模型
    class DeepLearningModel {
        constructor() {
            this.neuralNetwork = this.buildNeuralNetwork();
            this.trainingData = [];
            this.modelWeights = this.initializeWeights();
        }

        // 构建深度神经网络
        buildNeuralNetwork() {
            return {
                inputLayer: {
                    userFeatures: 50,    // 用户特征维度
                    itemFeatures: 30,    // 菜品特征维度
                    contextFeatures: 20  // 上下文特征维度
                },
                hiddenLayers: [
                    { neurons: 128, activation: 'relu', dropout: 0.3 },
                    { neurons: 64, activation: 'relu', dropout: 0.2 },
                    { neurons: 32, activation: 'tanh', dropout: 0.1 }
                ],
                outputLayer: {
                    neurons: 1,
                    activation: 'sigmoid'
                }
            };
        }

        // 初始化模型权重
        initializeWeights() {
            return {
                userEmbedding: this.randomWeights(50),
                itemEmbedding: this.randomWeights(30),
                contextEmbedding: this.randomWeights(20),
                hiddenWeights: [
                    this.randomWeights(128),
                    this.randomWeights(64),
                    this.randomWeights(32)
                ],
                outputWeights: this.randomWeights(1)
            };
        }

        // 前向传播
        forwardPropagation(userFeatures, itemFeatures, contextFeatures) {
            // 特征嵌入
            const userEmbed = this.embedFeatures(userFeatures, this.modelWeights.userEmbedding);
            const itemEmbed = this.embedFeatures(itemFeatures, this.modelWeights.itemEmbedding);
            const contextEmbed = this.embedFeatures(contextFeatures, this.modelWeights.contextEmbedding);
            
            // 特征融合
            const combinedFeatures = [...userEmbed, ...itemEmbed, ...contextEmbed];
            
            // 隐藏层计算
            let hidden = combinedFeatures;
            for (let i = 0; i < this.neuralNetwork.hiddenLayers.length; i++) {
                hidden = this.activateLayer(hidden, this.modelWeights.hiddenWeights[i], 
                    this.neuralNetwork.hiddenLayers[i].activation);
                hidden = this.applyDropout(hidden, this.neuralNetwork.hiddenLayers[i].dropout);
            }
            
            // 输出层
            const output = this.activateLayer(hidden, this.modelWeights.outputWeights, 'sigmoid');
            return output[0];
        }

        // 反向传播训练
        backPropagation(userFeatures, itemFeatures, contextFeatures, target, learningRate = 0.001) {
            const prediction = this.forwardPropagation(userFeatures, itemFeatures, contextFeatures);
            const error = target - prediction;
            
            // 计算梯度并更新权重
            const gradients = this.computeGradients(userFeatures, itemFeatures, contextFeatures, error);
            this.updateWeights(gradients, learningRate);
            
            return { prediction, error, loss: Math.pow(error, 2) };
        }

        // 预测用户对菜品的偏好分数
        predict(userId, itemId, context = {}) {
            const userFeatures = this.extractUserFeatures(userId);
            const itemFeatures = this.extractItemFeatures(itemId);
            const contextFeatures = this.extractContextFeatures(context);
            
            return this.forwardPropagation(userFeatures, itemFeatures, contextFeatures);
        }

        // 提取用户特征
        extractUserFeatures(userId) {
            const user = this.getUserData(userId);
            const behavior = this.getUserBehavior(userId);
            
            return [
                user.age || 25,
                user.gender === 'male' ? 1 : 0,
                user.income || 5000,
                behavior.totalOrders || 0,
                behavior.avgOrderValue || 0,
                behavior.favoriteCategories.length,
                behavior.timePatterns.morning || 0,
                behavior.timePatterns.lunch || 0,
                behavior.timePatterns.dinner || 0,
                behavior.priceSensitivity || 0.5,
                behavior.spicePreference || 0.5,
                behavior.healthConsciousness || 0.5,
                // ... 更多特征
                ...Array(37).fill(0) // 填充到50维
            ];
        }

        // 提取菜品特征
        extractItemFeatures(itemId) {
            const item = this.getItemData(itemId);
            
            return [
                item.price,
                item.rating,
                item.sales,
                item.cookingTime,
                this.encodeCategory(item.category),
                this.encodeTags(item.tags),
                item.isHot ? 1 : 0,
                item.hasDiscount ? 1 : 0,
                item.stock > 0 ? 1 : 0,
                // ... 更多特征
                ...Array(20).fill(0) // 填充到30维
            ];
        }

        // 提取上下文特征
        extractContextFeatures(context) {
            const now = new Date();
            
            return [
                now.getHours() / 24,
                now.getDay() / 7,
                now.getMonth() / 12,
                context.temperature || 20,
                context.humidity || 50,
                context.weather === 'sunny' ? 1 : 0,
                context.weather === 'rainy' ? 1 : 0,
                context.weather === 'cloudy' ? 1 : 0,
                context.season === 'spring' ? 1 : 0,
                context.season === 'summer' ? 1 : 0,
                context.season === 'autumn' ? 1 : 0,
                context.season === 'winter' ? 1 : 0,
                // ... 更多上下文特征
                ...Array(8).fill(0) // 填充到20维
            ];
        }
    }

    // 协同过滤推荐
    class CollaborativeFiltering {
        constructor() {
            this.userItemMatrix = new Map();
            this.itemSimilarity = new Map();
            this.userSimilarity = new Map();
        }

        // 计算用户相似度
        calculateUserSimilarity(userId1, userId2) {
            const user1Items = this.getUserItems(userId1);
            const user2Items = this.getUserItems(userId2);
            
            if (user1Items.length === 0 || user2Items.length === 0) return 0;
            
            const commonItems = this.getCommonItems(user1Items, user2Items);
            if (commonItems.length === 0) return 0;
            
            // 使用皮尔逊相关系数
            return this.pearsonCorrelation(commonItems, user1Items, user2Items);
        }

        // 计算菜品相似度
        calculateItemSimilarity(itemId1, itemId2) {
            const item1Users = this.getItemUsers(itemId1);
            const item2Users = this.getItemUsers(itemId2);
            
            if (item1Users.length === 0 || item2Users.length === 0) return 0;
            
            const commonUsers = this.getCommonUsers(item1Users, item2Users);
            if (commonUsers.length === 0) return 0;
            
            return this.cosineSimilarity(commonUsers, item1Users, item2Users);
        }

        // 基于用户的协同过滤
        userBasedRecommendation(userId, topN = 10) {
            const userItems = this.getUserItems(userId);
            const candidateItems = this.getCandidateItems(userItems);
            const scores = new Map();
            
            for (const itemId of candidateItems) {
                let score = 0;
                let weightSum = 0;
                
                // 找到相似用户
                const similarUsers = this.findSimilarUsers(userId, 20);
                
                for (const [similarUserId, similarity] of similarUsers) {
                    const rating = this.getUserItemRating(similarUserId, itemId);
                    if (rating > 0) {
                        score += similarity * rating;
                        weightSum += Math.abs(similarity);
                    }
                }
                
                if (weightSum > 0) {
                    scores.set(itemId, score / weightSum);
                }
            }
            
            return this.getTopItems(scores, topN);
        }

        // 基于物品的协同过滤
        itemBasedRecommendation(userId, topN = 10) {
            const userItems = this.getUserItems(userId);
            const candidateItems = this.getCandidateItems(userItems);
            const scores = new Map();
            
            for (const itemId of candidateItems) {
                let score = 0;
                let weightSum = 0;
                
                for (const [ratedItemId, rating] of userItems) {
                    const similarity = this.calculateItemSimilarity(itemId, ratedItemId);
                    if (similarity > 0) {
                        score += similarity * rating;
                        weightSum += Math.abs(similarity);
                    }
                }
                
                if (weightSum > 0) {
                    scores.set(itemId, score / weightSum);
                }
            }
            
            return this.getTopItems(scores, topN);
        }
    }

    // 基于内容的过滤
    class ContentBasedFiltering {
        constructor() {
            this.itemProfiles = new Map();
            this.userProfiles = new Map();
            this.tfidfVectorizer = new TFIDFVectorizer();
        }

        // 构建菜品画像
        buildItemProfile(itemId) {
            const item = this.getItemData(itemId);
            const profile = {
                category: item.category,
                tags: item.tags,
                price: item.price,
                rating: item.rating,
                description: item.description,
                ingredients: item.ingredients,
                cookingTime: item.cookingTime,
                features: this.extractItemFeatures(item)
            };
            
            this.itemProfiles.set(itemId, profile);
            return profile;
        }

        // 构建用户画像
        buildUserProfile(userId) {
            const userBehavior = this.getUserBehavior(userId);
            const profile = {
                favoriteCategories: this.extractFavoriteCategories(userBehavior),
                pricePreference: this.extractPricePreference(userBehavior),
                timePatterns: this.extractTimePatterns(userBehavior),
                dietaryRestrictions: this.extractDietaryRestrictions(userBehavior),
                features: this.extractUserFeatures(userBehavior)
            };
            
            this.userProfiles.set(userId, profile);
            return profile;
        }

        // 基于内容的推荐
        contentBasedRecommendation(userId, topN = 10) {
            const userProfile = this.getUserProfile(userId);
            const allItems = this.getAllItems();
            const scores = new Map();
            
            for (const itemId of allItems) {
                const itemProfile = this.getItemProfile(itemId);
                const similarity = this.calculateContentSimilarity(userProfile, itemProfile);
                scores.set(itemId, similarity);
            }
            
            return this.getTopItems(scores, topN);
        }

        // 计算内容相似度
        calculateContentSimilarity(userProfile, itemProfile) {
            let similarity = 0;
            let weightSum = 0;
            
            // 类别匹配
            const categoryMatch = this.calculateCategorySimilarity(userProfile.favoriteCategories, itemProfile.category);
            similarity += categoryMatch * 0.3;
            weightSum += 0.3;
            
            // 价格匹配
            const priceMatch = this.calculatePriceSimilarity(userProfile.pricePreference, itemProfile.price);
            similarity += priceMatch * 0.2;
            weightSum += 0.2;
            
            // 特征匹配
            const featureMatch = this.calculateFeatureSimilarity(userProfile.features, itemProfile.features);
            similarity += featureMatch * 0.5;
            weightSum += 0.5;
            
            return weightSum > 0 ? similarity / weightSum : 0;
        }
    }

    // 实时推荐引擎
    class RealTimeRecommendation {
        constructor() {
            this.streamProcessor = new StreamProcessor();
            this.realTimeCache = new Map();
            this.eventQueue = [];
            this.setupRealTimeProcessing();
        }

        // 设置实时处理
        setupRealTimeProcessing() {
            setInterval(() => {
                this.processEventQueue();
            }, 1000); // 每秒处理一次事件队列
        }

        // 处理实时事件
        processEvent(event) {
            this.eventQueue.push({
                ...event,
                timestamp: Date.now()
            });
        }

        // 处理事件队列
        processEventQueue() {
            if (this.eventQueue.length === 0) return;
            
            const events = this.eventQueue.splice(0, 100); // 批量处理
            this.updateRealTimeRecommendations(events);
        }

        // 更新实时推荐
        updateRealTimeRecommendations(events) {
            for (const event of events) {
                switch (event.type) {
                    case 'click':
                        this.handleClickEvent(event);
                        break;
                    case 'view':
                        this.handleViewEvent(event);
                        break;
                    case 'order':
                        this.handleOrderEvent(event);
                        break;
                    case 'search':
                        this.handleSearchEvent(event);
                        break;
                }
            }
        }

        // 生成实时推荐
        generateRealTimeRecommendations(userId, context = {}) {
            const cacheKey = `${userId}_${JSON.stringify(context)}`;
            
            // 检查缓存
            if (this.realTimeCache.has(cacheKey)) {
                const cached = this.realTimeCache.get(cacheKey);
                if (Date.now() - cached.timestamp < 30000) { // 30秒缓存
                    return cached.recommendations;
                }
            }
            
            // 生成新推荐
            const recommendations = this.computeRealTimeRecommendations(userId, context);
            
            // 更新缓存
            this.realTimeCache.set(cacheKey, {
                recommendations,
                timestamp: Date.now()
            });
            
            return recommendations;
        }
    }

    // 情感分析器
    class SentimentAnalyzer {
        constructor() {
            this.sentimentModel = this.loadSentimentModel();
            this.emotionLexicon = this.loadEmotionLexicon();
        }

        // 分析用户评论情感
        analyzeSentiment(text) {
            const words = this.tokenize(text);
            let positiveScore = 0;
            let negativeScore = 0;
            let neutralScore = 0;
            
            for (const word of words) {
                const sentiment = this.getWordSentiment(word);
                switch (sentiment) {
                    case 'positive':
                        positiveScore++;
                        break;
                    case 'negative':
                        negativeScore++;
                        break;
                    default:
                        neutralScore++;
                }
            }
            
            const total = positiveScore + negativeScore + neutralScore;
            if (total === 0) return { sentiment: 'neutral', confidence: 0 };
            
            const scores = {
                positive: positiveScore / total,
                negative: negativeScore / total,
                neutral: neutralScore / total
            };
            
            const maxSentiment = Object.keys(scores).reduce((a, b) => scores[a] > scores[b] ? a : b);
            return {
                sentiment: maxSentiment,
                confidence: scores[maxSentiment],
                scores
            };
        }

        // 分析菜品描述情感
        analyzeItemSentiment(itemId) {
            const item = this.getItemData(itemId);
            const description = item.description;
            return this.analyzeSentiment(description);
        }

        // 分析用户偏好情感
        analyzeUserPreferenceSentiment(userId) {
            const userReviews = this.getUserReviews(userId);
            const sentiments = userReviews.map(review => this.analyzeSentiment(review.text));
            
            return {
                avgPositive: sentiments.reduce((sum, s) => sum + s.scores.positive, 0) / sentiments.length,
                avgNegative: sentiments.reduce((sum, s) => sum + s.scores.negative, 0) / sentiments.length,
                overallSentiment: this.calculateOverallSentiment(sentiments)
            };
        }
    }

    // A/B测试管理器
    class ABTestManager {
        constructor() {
            this.activeTests = new Map();
            this.testResults = new Map();
            this.initializeABTests();
        }

        // 初始化A/B测试
        initializeABTests() {
            // 推荐算法A/B测试
            this.createABTest('recommendation_algorithm', {
                variants: {
                    A: { algorithm: 'collaborative_filtering', weight: 0.5 },
                    B: { algorithm: 'content_based', weight: 0.5 },
                    C: { algorithm: 'deep_learning', weight: 0.5 }
                },
                metrics: ['click_rate', 'conversion_rate', 'user_satisfaction']
            });

            // 推荐数量A/B测试
            this.createABTest('recommendation_count', {
                variants: {
                    A: { count: 5 },
                    B: { count: 10 },
                    C: { count: 15 }
                },
                metrics: ['engagement', 'conversion_rate']
            });
        }

        // 创建A/B测试
        createABTest(testName, config) {
            this.activeTests.set(testName, {
                ...config,
                startTime: Date.now(),
                participants: new Map()
            });
        }

        // 分配用户到测试组
        assignUserToTest(userId, testName) {
            const test = this.activeTests.get(testName);
            if (!test) return null;
            
            const hash = this.hashUserId(userId);
            const variantIndex = hash % Object.keys(test.variants).length;
            const variantName = Object.keys(test.variants)[variantIndex];
            
            test.participants.set(userId, {
                variant: variantName,
                assignedTime: Date.now()
            });
            
            return test.variants[variantName];
        }

        // 记录测试结果
        recordTestResult(userId, testName, metric, value) {
            const test = this.activeTests.get(testName);
            if (!test) return;
            
            const participant = test.participants.get(userId);
            if (!participant) return;
            
            const resultKey = `${testName}_${participant.variant}_${metric}`;
            if (!this.testResults.has(resultKey)) {
                this.testResults.set(resultKey, []);
            }
            
            this.testResults.get(resultKey).push({
                userId,
                value,
                timestamp: Date.now()
            });
        }

        // 分析测试结果
        analyzeTestResults(testName) {
            const test = this.activeTests.get(testName);
            if (!test) return null;
            
            const results = {};
            
            for (const [variantName, variant] of Object.entries(test.variants)) {
                results[variantName] = {};
                
                for (const metric of test.metrics) {
                    const resultKey = `${testName}_${variantName}_${metric}`;
                    const data = this.testResults.get(resultKey) || [];
                    
                    if (data.length > 0) {
                        results[variantName][metric] = {
                            mean: this.calculateMean(data.map(d => d.value)),
                            median: this.calculateMedian(data.map(d => d.value)),
                            std: this.calculateStd(data.map(d => d.value)),
                            count: data.length
                        };
                    }
                }
            }
            
            return results;
        }
    }

    // 高级推荐生成
    async generateAdvancedRecommendations(userId, options = {}) {
        const {
            count = 10,
            algorithm = 'hybrid',
            realTime = true,
            abTest = true
        } = options;

        // A/B测试分配
        let testConfig = null;
        if (abTest) {
            testConfig = this.abTestManager.assignUserToTest(userId, 'recommendation_algorithm');
        }

        // 获取用户上下文
        const context = await this.getUserContext(userId);
        
        // 多算法融合推荐
        const recommendations = await this.generateHybridRecommendations(userId, context, count, testConfig);
        
        // 实时调整
        if (realTime) {
            const realTimeAdjustments = this.realTimeEngine.generateRealTimeRecommendations(userId, context);
            this.mergeRecommendations(recommendations, realTimeAdjustments);
        }
        
        // 情感分析调整
        const sentimentAdjustments = this.applySentimentAnalysis(userId, recommendations);
        this.mergeRecommendations(recommendations, sentimentAdjustments);
        
        // 排序和过滤
        const finalRecommendations = this.rankAndFilterRecommendations(recommendations, userId, context);
        
        // 记录推荐结果用于学习
        this.recordRecommendationForLearning(userId, finalRecommendations, context);
        
        return {
            recommendations: finalRecommendations.slice(0, count),
            algorithm: algorithm,
            confidence: this.calculateRecommendationConfidence(finalRecommendations),
            explanation: this.generateRecommendationExplanation(finalRecommendations, userId),
            abTestVariant: testConfig?.algorithm || null
        };
    }

    // 混合推荐算法
    async generateHybridRecommendations(userId, context, count, testConfig) {
        const algorithms = testConfig ? [testConfig.algorithm] : ['collaborative', 'content', 'deep_learning'];
        const allRecommendations = new Map();
        
        for (const algorithm of algorithms) {
            let recommendations = [];
            
            switch (algorithm) {
                case 'collaborative':
                    recommendations = this.collaborativeFilter.userBasedRecommendation(userId, count);
                    break;
                case 'content':
                    recommendations = this.contentFilter.contentBasedRecommendation(userId, count);
                    break;
                case 'deep_learning':
                    recommendations = await this.generateDeepLearningRecommendations(userId, context, count);
                    break;
            }
            
            // 合并推荐结果
            for (const [itemId, score] of recommendations) {
                const currentScore = allRecommendations.get(itemId) || 0;
                allRecommendations.set(itemId, currentScore + score * (testConfig?.weight || 1.0));
            }
        }
        
        return Array.from(allRecommendations.entries())
            .sort((a, b) => b[1] - a[1]);
    }

    // 深度学习推荐
    async generateDeepLearningRecommendations(userId, context, count) {
        const allItems = this.getAllItems();
        const scores = new Map();
        
        for (const itemId of allItems) {
            const score = this.model.predict(userId, itemId, context);
            scores.set(itemId, score);
        }
        
        return Array.from(scores.entries())
            .sort((a, b) => b[1] - a[1])
            .slice(0, count);
    }

    // 应用情感分析
    applySentimentAnalysis(userId, recommendations) {
        const userSentiment = this.sentimentAnalyzer.analyzeUserPreferenceSentiment(userId);
        const adjustments = new Map();
        
        for (const [itemId, score] of recommendations) {
            const itemSentiment = this.sentimentAnalyzer.analyzeItemSentiment(itemId);
            
            // 根据情感匹配调整分数
            let adjustment = 1.0;
            if (userSentiment.overallSentiment === 'positive' && itemSentiment.sentiment === 'positive') {
                adjustment = 1.2;
            } else if (userSentiment.overallSentiment === 'negative' && itemSentiment.sentiment === 'negative') {
                adjustment = 1.1;
            }
            
            adjustments.set(itemId, score * adjustment);
        }
        
        return adjustments;
    }

    // 计算推荐置信度
    calculateRecommendationConfidence(recommendations) {
        if (recommendations.length === 0) return 0;
        
        const scores = recommendations.map(([_, score]) => score);
        const mean = scores.reduce((sum, score) => sum + score, 0) / scores.length;
        const variance = scores.reduce((sum, score) => sum + Math.pow(score - mean, 2), 0) / scores.length;
        const std = Math.sqrt(variance);
        
        return Math.min(1.0, mean / (std + 0.1));
    }

    // 生成推荐解释
    generateRecommendationExplanation(recommendations, userId) {
        const explanations = [];
        
        for (const [itemId, score] of recommendations.slice(0, 3)) {
            const item = this.getItemData(itemId);
            const userBehavior = this.getUserBehavior(userId);
            
            let explanation = `推荐${item.name}，因为：`;
            const reasons = [];
            
            // 基于历史偏好
            if (userBehavior.favoriteCategories.includes(item.category)) {
                reasons.push(`您喜欢${item.category}类菜品`);
            }
            
            // 基于评分
            if (item.rating >= 4.5) {
                reasons.push('高评分菜品');
            }
            
            // 基于价格
            if (item.price <= userBehavior.avgOrderValue * 1.2) {
                reasons.push('符合您的消费水平');
            }
            
            // 基于时间
            const currentHour = new Date().getHours();
            if ((currentHour >= 11 && currentHour <= 14) && item.category.includes('主食')) {
                reasons.push('午餐时间推荐');
            }
            
            explanation += reasons.join('、');
            explanations.push(explanation);
        }
        
        return explanations;
    }

    // 持续学习
    startContinuousLearning() {
        setInterval(() => {
            this.performContinuousLearning();
        }, 300000); // 每5分钟学习一次
    }

    // 执行持续学习
    async performContinuousLearning() {
        try {
            // 获取新的训练数据
            const newTrainingData = this.collectNewTrainingData();
            
            if (newTrainingData.length > 0) {
                // 更新深度学习模型
                await this.updateDeepLearningModel(newTrainingData);
                
                // 更新协同过滤模型
                this.updateCollaborativeFiltering();
                
                // 更新内容过滤模型
                this.updateContentFiltering();
                
                console.log(`持续学习完成，处理了 ${newTrainingData.length} 条新数据`);
            }
        } catch (error) {
            console.error('持续学习失败:', error);
        }
    }

    // 工具方法
    randomWeights(size) {
        return Array(size).fill(0).map(() => Math.random() * 0.1 - 0.05);
    }

    hashUserId(userId) {
        let hash = 0;
        for (let i = 0; i < userId.toString().length; i++) {
            const char = userId.toString().charCodeAt(i);
            hash = ((hash << 5) - hash) + char;
            hash = hash & hash; // 转换为32位整数
        }
        return Math.abs(hash);
    }

    calculateMean(values) {
        return values.reduce((sum, val) => sum + val, 0) / values.length;
    }

    calculateMedian(values) {
        const sorted = values.sort((a, b) => a - b);
        const mid = Math.floor(sorted.length / 2);
        return sorted.length % 2 === 0 ? (sorted[mid - 1] + sorted[mid]) / 2 : sorted[mid];
    }

    calculateStd(values) {
        const mean = this.calculateMean(values);
        const variance = values.reduce((sum, val) => sum + Math.pow(val - mean, 2), 0) / values.length;
        return Math.sqrt(variance);
    }
}

// 创建全局高级AI引擎实例
window.advancedAIEngine = new AdvancedAIEngine();
